## Archive for infinite variance estimators

## sampling-importance-resampling is not equivalent to exact sampling [triste SIR]

Posted in Books, Kids, Statistics, University life with tags asymptotics, cross validated, importance sampling, infinite variance estimators, sampling w/o replacement, self-normalised importance sampling, SIR on December 16, 2019 by xi'an**F**ollowing an X validated question on the topic, I reassessed a previous impression I had that sampling-importance-resampling (SIR) is equivalent to direct sampling for a given sample size. (As suggested in the above fit between a N(2,½) target and a N(0,1) proposal.) Indeed, when one produces a sample

and resamples with replacement from this sample using the importance weights

the resulting sample

is neither “i.” nor “i.d.” since the resampling step involves a self-normalisation of the weights and hence a global bias in the evaluation of expectations. In particular, if the importance function g is a poor choice for the target f, meaning that the exploration of the whole support is imperfect, if possible (when both supports are equal), a given sample may well fail to reproduce the properties of an iid example ,as shown in the graph below where a Normal density is used for g while f is a Student t⁵ density:

## improved importance sampling via iterated moment matching

Posted in Statistics with tags curse of dimensionality, finite variance, importance sampling, infinite variance estimators, Pareto smoothed importance sampling on August 1, 2019 by xi'an**T**opi Paananen, Juho Piironen, Paul-Christian Bürkner and Aki Vehtari have recently arXived a work on constructing an adapted importance (sampling) distribution. The beginning is more a review than a new contribution, covering the earlier work by Vehtari, Gelman and Gabri (2017): estimating the Pareto rate for the importance weight distribution helps in assessing whether or not this distribution allows for a (necessary) second moment. In case it does not (seem to), the authors propose an affine transform of the importance distribution, using the earlier sample to match the first two moments of the distribution. Or of the targeted function. Adaptation that is controlled by the same Pareto rate technique, as in the above picture (from the paper). Predicting a natural objection as to the poor performances of the earlier samples, the paper suggests to use robust estimators of these moments, for instance via Pareto smoothing. It also suggests using multiple importance sampling as a way to regularise and robustify the estimates. While I buy the argument of fitting the target moments to achieve a better fit of the importance sampling, I remain unclear as to why an affine transform would change the (poor) tail behaviour of the importance sampler. Hence why it would apply in full generality. An alternative could consist in finding appropriate Box-Cox transforms, although the difficulty would certainly increase with the dimension.

## Gibbs clashes with importance sampling

Posted in pictures, Statistics with tags Amsterdam, cross validated, Gibbs sampling, importance sampling, infinite variance estimators, normalising constant on April 11, 2019 by xi'an**I**n an X validated question, an interesting proposal was made: at each (component-wise) step of a Gibbs sampler, replace simulation from the exact full conditional with simulation from an alternate density and weight the resulting simulation with a term made of a product of (a) the previous weight (b) the ratio of the true conditional over the substitute for the new value and (c) the inverse ratio for the earlier value of the same component. Which does not work for several reasons:

- the reweighting is doomed by its very propagation in that it keeps multiplying ratios of expectation one, which means an almost sure chance of degenerating;
- the weights are computed for a previous value that has not been generated from the same proposal and is anyway already properly weighted;
- due to the change in dimension produced by Gibbs, the actual target is the full conditional, which involves an intractable normalising constant;
- there is no guarantee for the weights to have finite variance, esp. when the proposal has thinner tails than the target.

as can be readily checked by a quick simulation experiment. The funny thing is that a proper importance weight can be constructed when envisioning the sequence of Gibbs steps as a Metropolis proposal (in the dimension of the target). Sad enough, the person asking the question seems to have lost interest in the issue, a rather common occurrence on X validated!

## importance sampling and necessary sample size

Posted in Books, Statistics with tags arXiv, efficient importance sampling, infinite variance estimators, Monte Carlo approximations, Monte Carlo Statistical Methods, Persi Diaconis on September 7, 2016 by xi'an**D**aniel Sanz-Alonso arXived a note yesterday where he analyses importance sampling from the point of view of empirical distributions. With the difficulty that unnormalised importance sampling estimators are not associated with an empirical distribution since the sum of the weights is not one. For several f-divergences, he obtains upper bounds on those divergences between the empirical cdf and a uniform version, D(w,u), which translate into lower bounds on the importance sample size. I however do not see why this divergence between a weighted sampled and the uniformly weighted version is relevant for the divergence between the target and the proposal, nor how the resulting Monte Carlo estimator is impacted by this bound. A side remark [in the paper] is that those results apply to infinite variance Monte Carlo estimators, as in the recent paper of Chatterjee and Diaconis I discussed earlier, which also discussed the necessary sample size.